1. Data Manipulation & Analysis Excel – for quick data analysis
pivot tables
lookups
etc. SQL – to extract
filter
and manipulate data from databases. Python or R – for advanced data analysis
automation
and statistical operations. Libraries like: pandas
numpy
matplotlib
seaborn 2. Data Visualization Tools: Tableau
Power BI
Looker
or Python libraries (matplotlib
seaborn
plotly) Ability to communicate insights visually and effectively. 3. Statistics & Mathematics Descriptive statistics (mean
median
mode
standard deviation) Probability theory Hypothesis testing
regression
and basic inferential statistics 4. Database Management Understanding relational databases Writing efficient queries Basic knowledge of ETL (Extract
Transform
Load) processes 🔍 Analytical & Critical Thinking Skills Problem-solving – framing problems clearly and choosing appropriate analytical methods Data cleaning – identifying and correcting errors in data Data interpretation – making sense of patterns
trends
and anomalies 🧠 Business & Domain Knowledge Understanding the business context of data Translating data insights into business decisions Familiarity with KPIs
metrics
and ROI